Join Barton Poulson for an in-depth discussion in this video Clustering in R, part of Data Science Foundations: Data Mining.
- [Narrator] Now, what we have here…are the 48 contiguous states.…Washington, D.C. and Hawaii and Alaska…were not included in these data sets.…I have the name of the state, the state code,…and then, a whole bunch of searches…that I personally just chose off the top of my head.…Searches for data science, cluster analysis,…then college, startup, entrepreneur, CEO,…mortgage cause it might have something to do with finances.…Then, I have a few sports terms.…Searches for NBA, NFL, MLB,…Major League Baseball, and FIFA for soccer.…
Then, because I like it, I have searches for modern dance.…And then, I have three cars:…Prius, Escalade, Subaru.…Then, I have a few other, you know, kind of silly ones.…I have jello and BBQ and the royal family.…And then, two completely irrelevant terms,…obfuscation and unicorns.…Then, what I have is information…from a published research paper…about five personality dimensions…measured at a state level,…extroversion, agreeableness, contentiousness,…neuroticism, and openness.…
And then, states are put into…
Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.
- Prerequisites for data mining
- Data mining using R, Python, Orange, and RapidMiner
- Data reduction
- Data clustering
- Anomaly detection
- Association analysis
- Regression analysis
- Sequence mining
- Text mining
Skill Level Beginner
Transitioning from Data Warehousing to Big Datawith Alan Simon1h 50m Intermediate
Big Data Foundations: Program Managementwith Alan Simon1h 11m Intermediate
2. Data Reduction
5. Anomaly Detection
6. Association Analysis
7. Regression Analysis
8. Sequential Patterns
9. Text Mining
Next steps1m 18s
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